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In a localization network, the line-of-sight between anchors (transceivers)and targets may be blocked due to the presence of obstacles in the environment.Due to the non-zero size of the obstacles, the blocking is typically correlatedacross both anchor and target locations, with the extent of correlationincreasing with obstacle size. If a target does not have line-of-sight to aminimum number of anchors, then its position cannot be estimated unambiguouslyand is, therefore, said to be in a blind-spot. However, the analysis of theblind-spot probability of a given target is challenging due to the inherentrandomness in the obstacle locations and sizes. In this letter, we develop anew framework to analyze the worst-case impact of correlated blocking on theblind-spot probability of a typical target; in particular, we model theobstacles by a Poisson line process and the anchor locations by a Poisson pointprocess. For this setup, we define the notion of the asymptotic blind-spotprobability of the typical target and derive a closed-form expression for it asa function of the area distribution of a typical Poisson-Voronoi cell. As anupper bound for the more realistic case when obstacles have finite dimensions,the asymptotic blind-spot probability is useful as a design tool to ensure thatthe blind-spot probability of a typical target does not exceed a desiredthreshold, $\epsilon$.

Asymptotic Blind-spot Analysis of Localization Networks under Correlated Blocking using a Poisson Line Process